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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
# Load the base model and tokenizer
def load_model():
base_model = AutoModelForCausalLM.from_pretrained(
"microsoft/phi-2",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
# Load the fine-tuned adapter
model = PeftModel.from_pretrained(
base_model,
"satyanayak/PHI2-SFT-OASST1",
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
"microsoft/phi-2",
trust_remote_code=True
)
return model, tokenizer
# Generate response
def generate_response(prompt, max_length=512, temperature=0.7, top_p=0.9):
inputs = tokenizer(f"Human: {prompt}\nAssistant:", return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the Assistant's response
response = response.split("Assistant:")[-1].strip()
return response
# Example prompts - Update to include values for all input parameters
EXAMPLE_PROMPTS = [
["What is the capital of France?", 512, 0.7, 0.9],
["Write a short poem about autumn.", 512, 0.7, 0.9],
["Explain quantum computing in simple terms.", 512, 0.7, 0.9],
["Give me a recipe for chocolate chip cookies.", 512, 0.7, 0.9],
["What are the benefits of regular exercise?", 512, 0.7, 0.9]
]
# Load model and tokenizer
print("Loading model...")
model, tokenizer = load_model()
print("Model loaded!")
# Create Gradio interface
demo = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(
label="Enter your prompt",
placeholder="Type your message here...",
lines=4
),
gr.Slider(
minimum=64,
maximum=1024,
value=512,
step=64,
label="Maximum Length"
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.7,
step=0.1,
label="Temperature"
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.1,
label="Top P"
)
],
outputs=gr.Textbox(label="Response", lines=10),
examples=EXAMPLE_PROMPTS,
title="Phi-2 Assistant",
description="This is a fine-tuned version of Phi-2 on the OpenAssistant dataset. Enter your prompt and adjust generation parameters as needed.",
)
# Add this line at the end of the file
if __name__ == "__main__":
demo.launch(share=True) |